Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations239671
Missing cells285626
Missing cells (%)4.6%
Duplicate rows697
Duplicate rows (%)0.3%
Total size in memory47.5 MiB
Average record size in memory208.0 B

Variable types

Numeric10
Text8
Categorical5
DateTime3

Alerts

Dataset has 697 (0.3%) duplicate rowsDuplicates
action is highly overall correlated with inspection_typeHigh correlation
bbl is highly overall correlated with bin and 5 other fieldsHigh correlation
bin is highly overall correlated with bbl and 5 other fieldsHigh correlation
boro is highly overall correlated with bbl and 4 other fieldsHigh correlation
census_tract is highly overall correlated with bbl and 5 other fieldsHigh correlation
community_board is highly overall correlated with bbl and 6 other fieldsHigh correlation
council_district is highly overall correlated with bbl and 6 other fieldsHigh correlation
critical_flag is highly overall correlated with inspection_typeHigh correlation
inspection_type is highly overall correlated with action and 1 other fieldsHigh correlation
latitude is highly overall correlated with council_districtHigh correlation
longitude is highly overall correlated with census_tract and 2 other fieldsHigh correlation
zipcode is highly overall correlated with bbl and 6 other fieldsHigh correlation
action is highly imbalanced (85.1%) Imbalance
inspection_type is highly imbalanced (58.6%) Imbalance
zipcode has 2416 (1.0%) missing values Missing
score has 9228 (3.9%) missing values Missing
community_board has 2875 (1.2%) missing values Missing
council_district has 2865 (1.2%) missing values Missing
census_tract has 2865 (1.2%) missing values Missing
bin has 4099 (1.7%) missing values Missing
nta has 2875 (1.2%) missing values Missing
grade has 123298 (51.4%) missing values Missing
grade_date has 131148 (54.7%) missing values Missing
score has 8788 (3.7%) zeros Zeros
latitude has 2416 (1.0%) zeros Zeros
longitude has 2416 (1.0%) zeros Zeros

Reproduction

Analysis started2024-11-19 06:02:50.685275
Analysis finished2024-11-19 07:52:40.449030
Duration1 hour, 49 minutes and 49.76 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

camis
Real number (ℝ)

Distinct26225
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47702580
Minimum30075445
Maximum50159091
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-11-19T02:55:08.393884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum30075445
5-th percentile40673117
Q141686665
median50074627
Q350113280
95-th percentile50142413
Maximum50159091
Range20083646
Interquartile range (IQR)8426615

Descriptive statistics

Standard deviation3957181.2
Coefficient of variation (CV)0.082955286
Kurtosis-0.83362953
Mean47702580
Median Absolute Deviation (MAD)50524
Skewness-1.0638185
Sum1.1432925 × 1013
Variance1.5659283 × 1013
MonotonicityNot monotonic
2024-11-19T02:55:08.596090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40365904 66
 
< 0.1%
50111296 64
 
< 0.1%
50105561 64
 
< 0.1%
50123073 63
 
< 0.1%
50089474 61
 
< 0.1%
41406895 58
 
< 0.1%
50079599 56
 
< 0.1%
40714228 53
 
< 0.1%
50128889 51
 
< 0.1%
50111191 49
 
< 0.1%
Other values (26215) 239086
99.8%
ValueCountFrequency (%)
30075445 16
< 0.1%
30191841 5
 
< 0.1%
40356018 3
 
< 0.1%
40356483 22
< 0.1%
40356731 8
 
< 0.1%
40357217 4
 
< 0.1%
40359480 4
 
< 0.1%
40359705 13
< 0.1%
40360045 8
 
< 0.1%
40361618 7
 
< 0.1%
ValueCountFrequency (%)
50159091 1
 
< 0.1%
50158928 6
< 0.1%
50158842 4
 
< 0.1%
50158815 13
< 0.1%
50158791 2
 
< 0.1%
50158779 4
 
< 0.1%
50158777 6
< 0.1%
50158741 1
 
< 0.1%
50158730 3
 
< 0.1%
50158722 3
 
< 0.1%

dba
Text

Distinct20890
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2024-11-19T02:55:09.137380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length100
Median length63
Mean length16.032524
Min length2

Characters and Unicode

Total characters3842531
Distinct characters94
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique777 ?
Unique (%)0.3%

Sample

1st rowLA AURORA
2nd rowMAMA'S RESTAURANT
3rd rowNY 99 CENTS FRESH PIZZA
4th rowAUTHENTIC FLAVAZ
5th rowSUSHI TIME
ValueCountFrequency (%)
restaurant 26852
 
4.2%
21354
 
3.3%
cafe 12630
 
2.0%
pizza 11377
 
1.8%
bar 9830
 
1.5%
bakery 9043
 
1.4%
the 8509
 
1.3%
coffee 7370
 
1.1%
grill 6225
 
1.0%
dunkin 5119
 
0.8%
Other values (14456) 522921
81.5%
2024-11-19T02:55:09.857157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
402433
 
10.5%
A 382411
 
10.0%
E 345814
 
9.0%
R 241271
 
6.3%
N 235867
 
6.1%
I 228027
 
5.9%
S 216458
 
5.6%
O 204645
 
5.3%
T 202523
 
5.3%
L 149821
 
3.9%
Other values (84) 1233261
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3842531
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
402433
 
10.5%
A 382411
 
10.0%
E 345814
 
9.0%
R 241271
 
6.3%
N 235867
 
6.1%
I 228027
 
5.9%
S 216458
 
5.6%
O 204645
 
5.3%
T 202523
 
5.3%
L 149821
 
3.9%
Other values (84) 1233261
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3842531
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
402433
 
10.5%
A 382411
 
10.0%
E 345814
 
9.0%
R 241271
 
6.3%
N 235867
 
6.1%
I 228027
 
5.9%
S 216458
 
5.6%
O 204645
 
5.3%
T 202523
 
5.3%
L 149821
 
3.9%
Other values (84) 1233261
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3842531
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
402433
 
10.5%
A 382411
 
10.0%
E 345814
 
9.0%
R 241271
 
6.3%
N 235867
 
6.1%
I 228027
 
5.9%
S 216458
 
5.6%
O 204645
 
5.3%
T 202523
 
5.3%
L 149821
 
3.9%
Other values (84) 1233261
32.1%

boro
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Manhattan
87929 
Brooklyn
65128 
Queens
56338 
Bronx
21534 
Staten Island
 
8742

Length

Max length13
Median length9
Mean length7.8095765
Min length5

Characters and Unicode

Total characters1871729
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQueens
2nd rowQueens
3rd rowManhattan
4th rowBrooklyn
5th rowQueens

Common Values

ValueCountFrequency (%)
Manhattan 87929
36.7%
Brooklyn 65128
27.2%
Queens 56338
23.5%
Bronx 21534
 
9.0%
Staten Island 8742
 
3.6%

Length

2024-11-19T02:55:10.035623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T02:55:10.190993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
manhattan 87929
35.4%
brooklyn 65128
26.2%
queens 56338
22.7%
bronx 21534
 
8.7%
staten 8742
 
3.5%
island 8742
 
3.5%

Most occurring characters

ValueCountFrequency (%)
n 336342
18.0%
a 281271
15.0%
t 193342
10.3%
o 151790
8.1%
e 121418
 
6.5%
M 87929
 
4.7%
h 87929
 
4.7%
B 86662
 
4.6%
r 86662
 
4.6%
l 73870
 
3.9%
Other values (10) 364514
19.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1871729
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 336342
18.0%
a 281271
15.0%
t 193342
10.3%
o 151790
8.1%
e 121418
 
6.5%
M 87929
 
4.7%
h 87929
 
4.7%
B 86662
 
4.6%
r 86662
 
4.6%
l 73870
 
3.9%
Other values (10) 364514
19.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1871729
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 336342
18.0%
a 281271
15.0%
t 193342
10.3%
o 151790
8.1%
e 121418
 
6.5%
M 87929
 
4.7%
h 87929
 
4.7%
B 86662
 
4.6%
r 86662
 
4.6%
l 73870
 
3.9%
Other values (10) 364514
19.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1871729
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 336342
18.0%
a 281271
15.0%
t 193342
10.3%
o 151790
8.1%
e 121418
 
6.5%
M 87929
 
4.7%
h 87929
 
4.7%
B 86662
 
4.6%
r 86662
 
4.6%
l 73870
 
3.9%
Other values (10) 364514
19.5%
Distinct7357
Distinct (%)3.1%
Missing246
Missing (%)0.1%
Memory size1.8 MiB
2024-11-19T02:55:10.762752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length3.4706735
Min length1

Characters and Unicode

Total characters830966
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87 ?
Unique (%)< 0.1%

Sample

1st row23917
2nd row3708
3rd row12
4th row1377
5th row7242
ValueCountFrequency (%)
1 1097
 
0.5%
200 752
 
0.3%
2 671
 
0.3%
25 645
 
0.3%
55 609
 
0.3%
20 579
 
0.2%
100 578
 
0.2%
11 568
 
0.2%
30 565
 
0.2%
10 535
 
0.2%
Other values (7325) 233100
97.2%
2024-11-19T02:55:11.467899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 154824
18.6%
2 108233
13.0%
0 93214
11.2%
3 83432
10.0%
5 77692
9.3%
4 75449
9.1%
6 62372
7.5%
7 59264
 
7.1%
8 55386
 
6.7%
9 50433
 
6.1%
Other values (27) 10667
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 830966
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 154824
18.6%
2 108233
13.0%
0 93214
11.2%
3 83432
10.0%
5 77692
9.3%
4 75449
9.1%
6 62372
7.5%
7 59264
 
7.1%
8 55386
 
6.7%
9 50433
 
6.1%
Other values (27) 10667
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 830966
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 154824
18.6%
2 108233
13.0%
0 93214
11.2%
3 83432
10.0%
5 77692
9.3%
4 75449
9.1%
6 62372
7.5%
7 59264
 
7.1%
8 55386
 
6.7%
9 50433
 
6.1%
Other values (27) 10667
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 830966
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 154824
18.6%
2 108233
13.0%
0 93214
11.2%
3 83432
10.0%
5 77692
9.3%
4 75449
9.1%
6 62372
7.5%
7 59264
 
7.1%
8 55386
 
6.7%
9 50433
 
6.1%
Other values (27) 10667
 
1.3%

street
Text

Distinct2295
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2024-11-19T02:55:11.938639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length40
Median length37
Mean length12.943105
Min length5

Characters and Unicode

Total characters3102087
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)< 0.1%

Sample

1st rowBRADDOCK AVE
2nd row73RD ST
3rd rowPERRY STREET
4th rowEAST NEW YORK AVENUE
5th rowAUSTIN ST
ValueCountFrequency (%)
avenue 95872
 
18.2%
street 66391
 
12.6%
west 18020
 
3.4%
east 16843
 
3.2%
ave 16725
 
3.2%
blvd 11429
 
2.2%
broadway 10127
 
1.9%
boulevard 9013
 
1.7%
st 8733
 
1.7%
road 6692
 
1.3%
Other values (1426) 265730
50.6%
2024-11-19T02:55:12.611840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 495538
16.0%
343375
11.1%
T 268742
 
8.7%
A 267827
 
8.6%
N 197530
 
6.4%
R 194365
 
6.3%
S 181355
 
5.8%
V 144157
 
4.6%
U 138237
 
4.5%
O 112789
 
3.6%
Other values (60) 758172
24.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3102087
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 495538
16.0%
343375
11.1%
T 268742
 
8.7%
A 267827
 
8.6%
N 197530
 
6.4%
R 194365
 
6.3%
S 181355
 
5.8%
V 144157
 
4.6%
U 138237
 
4.5%
O 112789
 
3.6%
Other values (60) 758172
24.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3102087
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 495538
16.0%
343375
11.1%
T 268742
 
8.7%
A 267827
 
8.6%
N 197530
 
6.4%
R 194365
 
6.3%
S 181355
 
5.8%
V 144157
 
4.6%
U 138237
 
4.5%
O 112789
 
3.6%
Other values (60) 758172
24.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3102087
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 495538
16.0%
343375
11.1%
T 268742
 
8.7%
A 267827
 
8.6%
N 197530
 
6.4%
R 194365
 
6.3%
S 181355
 
5.8%
V 144157
 
4.6%
U 138237
 
4.5%
O 112789
 
3.6%
Other values (60) 758172
24.4%

zipcode
Real number (ℝ)

High correlation  Missing 

Distinct219
Distinct (%)0.1%
Missing2416
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean10709.42
Minimum10000
Maximum12345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-11-19T02:55:12.779438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile10003
Q110023
median11101
Q311231
95-th percentile11416
Maximum12345
Range2345
Interquartile range (IQR)1208

Descriptive statistics

Standard deviation593.05841
Coefficient of variation (CV)0.055377268
Kurtosis-1.815696
Mean10709.42
Median Absolute Deviation (MAD)334
Skewness-0.10268631
Sum2.5408634 × 109
Variance351718.28
MonotonicityNot monotonic
2024-11-19T02:55:13.061808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10003 5484
 
2.3%
10013 5434
 
2.3%
10019 4817
 
2.0%
10001 4762
 
2.0%
10036 4528
 
1.9%
11354 4513
 
1.9%
10002 4400
 
1.8%
11201 4277
 
1.8%
11220 3938
 
1.6%
11372 3854
 
1.6%
Other values (209) 191248
79.8%
ValueCountFrequency (%)
10000 18
 
< 0.1%
10001 4762
2.0%
10002 4400
1.8%
10003 5484
2.3%
10004 1163
 
0.5%
10005 659
 
0.3%
10006 392
 
0.2%
10007 1395
 
0.6%
10009 2552
1.1%
10010 2051
 
0.9%
ValueCountFrequency (%)
12345 7
 
< 0.1%
11697 16
 
< 0.1%
11694 246
 
0.1%
11693 223
 
0.1%
11692 87
 
< 0.1%
11691 420
 
0.2%
11436 223
 
0.1%
11435 1172
0.5%
11434 869
0.4%
11433 241
 
0.1%

phone
Text

Distinct24101
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2024-11-19T02:55:13.514596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2396710
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique636 ?
Unique (%)0.3%

Sample

1st row7183474271
2nd row3478917275
3rd row9172922325
4th row7189751121
5th row3479560001
ValueCountFrequency (%)
2126159700 144
 
0.1%
7182246030 131
 
0.1%
2122441111 126
 
0.1%
9082308846 115
 
< 0.1%
3477017760 105
 
< 0.1%
9178863304 103
 
< 0.1%
9175103862 103
 
< 0.1%
9177709055 92
 
< 0.1%
7043285184 91
 
< 0.1%
9175878888 90
 
< 0.1%
Other values (24091) 238571
99.5%
2024-11-19T02:55:14.112696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 310220
12.9%
1 306546
12.8%
2 294454
12.3%
8 284208
11.9%
6 224774
9.4%
4 213958
8.9%
3 202962
8.5%
9 201212
8.4%
0 188824
7.9%
5 168398
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2396710
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 310220
12.9%
1 306546
12.8%
2 294454
12.3%
8 284208
11.9%
6 224774
9.4%
4 213958
8.9%
3 202962
8.5%
9 201212
8.4%
0 188824
7.9%
5 168398
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2396710
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 310220
12.9%
1 306546
12.8%
2 294454
12.3%
8 284208
11.9%
6 224774
9.4%
4 213958
8.9%
3 202962
8.5%
9 201212
8.4%
0 188824
7.9%
5 168398
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2396710
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 310220
12.9%
1 306546
12.8%
2 294454
12.3%
8 284208
11.9%
6 224774
9.4%
4 213958
8.9%
3 202962
8.5%
9 201212
8.4%
0 188824
7.9%
5 168398
7.0%
Distinct89
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2024-11-19T02:55:14.483743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length30
Median length24
Mean length9.6617738
Min length4

Characters and Unicode

Total characters2315647
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSpanish
2nd rowBangladeshi
3rd rowPizza
4th rowCaribbean
5th rowJapanese
ValueCountFrequency (%)
american 48343
 
16.9%
chinese 23443
 
8.2%
coffee/tea 16865
 
5.9%
pizza 14865
 
5.2%
latin 10128
 
3.5%
mexican 9721
 
3.4%
bakery 9712
 
3.4%
products/desserts 9712
 
3.4%
caribbean 8897
 
3.1%
japanese 8099
 
2.8%
Other values (90) 127018
44.3%
2024-11-19T02:55:15.011684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 310597
 
13.4%
a 241972
 
10.4%
i 200604
 
8.7%
n 192637
 
8.3%
s 151929
 
6.6%
r 137964
 
6.0%
c 89710
 
3.9%
t 73919
 
3.2%
h 69611
 
3.0%
o 66994
 
2.9%
Other values (41) 779710
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2315647
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 310597
 
13.4%
a 241972
 
10.4%
i 200604
 
8.7%
n 192637
 
8.3%
s 151929
 
6.6%
r 137964
 
6.0%
c 89710
 
3.9%
t 73919
 
3.2%
h 69611
 
3.0%
o 66994
 
2.9%
Other values (41) 779710
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2315647
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 310597
 
13.4%
a 241972
 
10.4%
i 200604
 
8.7%
n 192637
 
8.3%
s 151929
 
6.6%
r 137964
 
6.0%
c 89710
 
3.9%
t 73919
 
3.2%
h 69611
 
3.0%
o 66994
 
2.9%
Other values (41) 779710
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2315647
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 310597
 
13.4%
a 241972
 
10.4%
i 200604
 
8.7%
n 192637
 
8.3%
s 151929
 
6.6%
r 137964
 
6.0%
c 89710
 
3.9%
t 73919
 
3.2%
h 69611
 
3.0%
o 66994
 
2.9%
Other values (41) 779710
33.7%
Distinct1271
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Minimum2019-01-03 00:00:00
Maximum2024-09-30 00:00:00
2024-11-19T02:55:15.194238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T02:55:15.379568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

action
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Violations were cited in the following area(s).
227528 
Establishment Closed by DOHMH. Violations were cited in the following area(s) and those requiring immediate action were addressed.
 
8869
Establishment re-opened by DOHMH.
 
1886
No violations were recorded at the time of this inspection.
 
1383
Establishment re-closed by DOHMH.
 
5

Length

Max length130
Median length47
Mean length50.030191
Min length33

Characters and Unicode

Total characters11990786
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowViolations were cited in the following area(s).
2nd rowViolations were cited in the following area(s).
3rd rowViolations were cited in the following area(s).
4th rowViolations were cited in the following area(s).
5th rowViolations were cited in the following area(s).

Common Values

ValueCountFrequency (%)
Violations were cited in the following area(s). 227528
94.9%
Establishment Closed by DOHMH. Violations were cited in the following area(s) and those requiring immediate action were addressed. 8869
 
3.7%
Establishment re-opened by DOHMH. 1886
 
0.8%
No violations were recorded at the time of this inspection. 1383
 
0.6%
Establishment re-closed by DOHMH. 5
 
< 0.1%

Length

2024-11-19T02:55:15.552108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T02:55:15.676773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
were 246649
13.9%
violations 237780
13.4%
the 237780
13.4%
cited 236397
13.3%
in 236397
13.3%
following 236397
13.3%
area(s 236397
13.3%
establishment 10760
 
0.6%
by 10760
 
0.6%
dohmh 10760
 
0.6%
Other values (16) 73655
 
4.2%

Most occurring characters

ValueCountFrequency (%)
1534061
12.8%
e 1287915
10.7%
i 1245388
10.4%
o 982384
 
8.2%
t 765616
 
6.4%
a 758193
 
6.3%
n 752593
 
6.3%
l 730208
 
6.1%
s 533944
 
4.5%
r 514310
 
4.3%
Other values (25) 2886174
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11990786
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1534061
12.8%
e 1287915
10.7%
i 1245388
10.4%
o 982384
 
8.2%
t 765616
 
6.4%
a 758193
 
6.3%
n 752593
 
6.3%
l 730208
 
6.1%
s 533944
 
4.5%
r 514310
 
4.3%
Other values (25) 2886174
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11990786
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1534061
12.8%
e 1287915
10.7%
i 1245388
10.4%
o 982384
 
8.2%
t 765616
 
6.4%
a 758193
 
6.3%
n 752593
 
6.3%
l 730208
 
6.1%
s 533944
 
4.5%
r 514310
 
4.3%
Other values (25) 2886174
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11990786
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1534061
12.8%
e 1287915
10.7%
i 1245388
10.4%
o 982384
 
8.2%
t 765616
 
6.4%
a 758193
 
6.3%
n 752593
 
6.3%
l 730208
 
6.1%
s 533944
 
4.5%
r 514310
 
4.3%
Other values (25) 2886174
24.1%
Distinct138
Distinct (%)0.1%
Missing1373
Missing (%)0.6%
Memory size1.8 MiB
2024-11-19T02:55:16.092717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.0718974
Min length3

Characters and Unicode

Total characters732027
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row04L
2nd row04A
3rd row10G
4th row08A
5th row04L
ValueCountFrequency (%)
10f 33519
14.1%
08a 24495
 
10.3%
06d 15965
 
6.7%
02g 14958
 
6.3%
04l 14425
 
6.1%
10b 14264
 
6.0%
06c 13828
 
5.8%
02b 12623
 
5.3%
04n 10862
 
4.6%
04a 7063
 
3.0%
Other values (128) 76296
32.0%
2024-11-19T02:55:16.666170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 241140
32.9%
1 65307
 
8.9%
4 50790
 
6.9%
6 46929
 
6.4%
2 39086
 
5.3%
A 38989
 
5.3%
F 38700
 
5.3%
8 32984
 
4.5%
B 31256
 
4.3%
C 23739
 
3.2%
Other values (17) 123107
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 732027
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 241140
32.9%
1 65307
 
8.9%
4 50790
 
6.9%
6 46929
 
6.4%
2 39086
 
5.3%
A 38989
 
5.3%
F 38700
 
5.3%
8 32984
 
4.5%
B 31256
 
4.3%
C 23739
 
3.2%
Other values (17) 123107
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 732027
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 241140
32.9%
1 65307
 
8.9%
4 50790
 
6.9%
6 46929
 
6.4%
2 39086
 
5.3%
A 38989
 
5.3%
F 38700
 
5.3%
8 32984
 
4.5%
B 31256
 
4.3%
C 23739
 
3.2%
Other values (17) 123107
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 732027
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 241140
32.9%
1 65307
 
8.9%
4 50790
 
6.9%
6 46929
 
6.4%
2 39086
 
5.3%
A 38989
 
5.3%
F 38700
 
5.3%
8 32984
 
4.5%
B 31256
 
4.3%
C 23739
 
3.2%
Other values (17) 123107
16.8%
Distinct216
Distinct (%)0.1%
Missing1373
Missing (%)0.6%
Memory size1.8 MiB
2024-11-19T02:55:17.084013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length952
Median length305
Mean length157.88182
Min length19

Characters and Unicode

Total characters37622923
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowEvidence of mice or live mice in establishment's food or non-food areas.
2nd rowFood Protection Certificate (FPC) not held by manager or supervisor of food operations.
3rd rowDishwashing and ware washing: Cleaning and sanitizing of tableware, including dishes, utensils, and equipment deficient.
4th rowEstablishment is not free of harborage or conditions conducive to rodents, insects or other pests.
5th rowEvidence of mice or live mice in establishment's food or non-food areas.
ValueCountFrequency (%)
or 420324
 
7.6%
not 258078
 
4.7%
food 142548
 
2.6%
and 131760
 
2.4%
flies 113384
 
2.0%
of 110335
 
2.0%
above 101696
 
1.8%
to 92850
 
1.7%
properly 88322
 
1.6%
in 82550
 
1.5%
Other values (844) 4005115
72.2%
2024-11-19T02:55:17.687450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5333481
14.2%
e 3589796
 
9.5%
o 3108525
 
8.3%
n 2388393
 
6.3%
a 2342883
 
6.2%
i 2298441
 
6.1%
r 2244760
 
6.0%
t 2211686
 
5.9%
s 1836395
 
4.9%
d 1664974
 
4.4%
Other values (66) 10603589
28.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37622923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5333481
14.2%
e 3589796
 
9.5%
o 3108525
 
8.3%
n 2388393
 
6.3%
a 2342883
 
6.2%
i 2298441
 
6.1%
r 2244760
 
6.0%
t 2211686
 
5.9%
s 1836395
 
4.9%
d 1664974
 
4.4%
Other values (66) 10603589
28.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37622923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5333481
14.2%
e 3589796
 
9.5%
o 3108525
 
8.3%
n 2388393
 
6.3%
a 2342883
 
6.2%
i 2298441
 
6.1%
r 2244760
 
6.0%
t 2211686
 
5.9%
s 1836395
 
4.9%
d 1664974
 
4.4%
Other values (66) 10603589
28.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37622923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5333481
14.2%
e 3589796
 
9.5%
o 3108525
 
8.3%
n 2388393
 
6.3%
a 2342883
 
6.2%
i 2298441
 
6.1%
r 2244760
 
6.0%
t 2211686
 
5.9%
s 1836395
 
4.9%
d 1664974
 
4.4%
Other values (66) 10603589
28.2%

critical_flag
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Critical
130539 
Not Critical
106393 
Not Applicable
 
2739

Length

Max length14
Median length8
Mean length9.8442198
Min length8

Characters and Unicode

Total characters2359374
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCritical
2nd rowCritical
3rd rowNot Critical
4th rowNot Critical
5th rowCritical

Common Values

ValueCountFrequency (%)
Critical 130539
54.5%
Not Critical 106393
44.4%
Not Applicable 2739
 
1.1%

Length

2024-11-19T02:55:17.984602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T02:55:18.252886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
critical 236932
67.9%
not 109132
31.3%
applicable 2739
 
0.8%

Most occurring characters

ValueCountFrequency (%)
i 476603
20.2%
t 346064
14.7%
l 242410
10.3%
c 239671
10.2%
a 239671
10.2%
r 236932
10.0%
C 236932
10.0%
N 109132
 
4.6%
o 109132
 
4.6%
109132
 
4.6%
Other values (4) 13695
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2359374
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 476603
20.2%
t 346064
14.7%
l 242410
10.3%
c 239671
10.2%
a 239671
10.2%
r 236932
10.0%
C 236932
10.0%
N 109132
 
4.6%
o 109132
 
4.6%
109132
 
4.6%
Other values (4) 13695
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2359374
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 476603
20.2%
t 346064
14.7%
l 242410
10.3%
c 239671
10.2%
a 239671
10.2%
r 236932
10.0%
C 236932
10.0%
N 109132
 
4.6%
o 109132
 
4.6%
109132
 
4.6%
Other values (4) 13695
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2359374
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 476603
20.2%
t 346064
14.7%
l 242410
10.3%
c 239671
10.2%
a 239671
10.2%
r 236932
10.0%
C 236932
10.0%
N 109132
 
4.6%
o 109132
 
4.6%
109132
 
4.6%
Other values (4) 13695
 
0.6%

score
Real number (ℝ)

Missing  Zeros 

Distinct136
Distinct (%)0.1%
Missing9228
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean23.951398
Minimum0
Maximum168
Zeros8788
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-11-19T02:55:18.417483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q112
median20
Q332
95-th percentile60
Maximum168
Range168
Interquartile range (IQR)20

Descriptive statistics

Standard deviation18.17703
Coefficient of variation (CV)0.7589131
Kurtosis4.0173809
Mean23.951398
Median Absolute Deviation (MAD)9
Skewness1.6168023
Sum5519432
Variance330.4044
MonotonicityNot monotonic
2024-11-19T02:55:18.594970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 19985
 
8.3%
13 18657
 
7.8%
10 9599
 
4.0%
11 8909
 
3.7%
0 8788
 
3.7%
9 8295
 
3.5%
7 6248
 
2.6%
27 6207
 
2.6%
22 5619
 
2.3%
26 5594
 
2.3%
Other values (126) 132542
55.3%
(Missing) 9228
 
3.9%
ValueCountFrequency (%)
0 8788
3.7%
2 2736
 
1.1%
3 1035
 
0.4%
4 2180
 
0.9%
5 3121
 
1.3%
6 1277
 
0.5%
7 6248
2.6%
8 3252
 
1.4%
9 8295
3.5%
10 9599
4.0%
ValueCountFrequency (%)
168 9
 
< 0.1%
154 12
 
< 0.1%
153 20
< 0.1%
143 14
 
< 0.1%
142 38
< 0.1%
141 9
 
< 0.1%
138 27
< 0.1%
136 24
< 0.1%
134 9
 
< 0.1%
132 15
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Minimum2024-11-18 06:00:10
Maximum2024-11-18 06:00:12
2024-11-19T02:55:18.748559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T02:55:18.879212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

inspection_type
Categorical

High correlation  Imbalance 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Cycle Inspection / Initial Inspection
130860 
Cycle Inspection / Re-inspection
44258 
Pre-permit (Operational) / Initial Inspection
36182 
Pre-permit (Operational) / Re-inspection
 
10203
Administrative Miscellaneous / Initial Inspection
 
6330
Other values (26)
 
11838

Length

Max length59
Median length37
Mean length38.089018
Min length25

Characters and Unicode

Total characters9128833
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCycle Inspection / Initial Inspection
2nd rowPre-permit (Operational) / Re-inspection
3rd rowCycle Inspection / Initial Inspection
4th rowCycle Inspection / Initial Inspection
5th rowCycle Inspection / Initial Inspection

Common Values

ValueCountFrequency (%)
Cycle Inspection / Initial Inspection 130860
54.6%
Cycle Inspection / Re-inspection 44258
 
18.5%
Pre-permit (Operational) / Initial Inspection 36182
 
15.1%
Pre-permit (Operational) / Re-inspection 10203
 
4.3%
Administrative Miscellaneous / Initial Inspection 6330
 
2.6%
Pre-permit (Non-operational) / Initial Inspection 3106
 
1.3%
Pre-permit (Operational) / Compliance Inspection 1718
 
0.7%
Cycle Inspection / Reopening Inspection 1444
 
0.6%
Administrative Miscellaneous / Re-inspection 1213
 
0.5%
Cycle Inspection / Compliance Inspection 991
 
0.4%
Other values (21) 3366
 
1.4%

Length

2024-11-19T02:55:19.065016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inspection 361176
31.6%
239671
21.0%
initial 178203
15.6%
cycle 177611
15.5%
re-inspection 56106
 
4.9%
pre-permit 52416
 
4.6%
operational 49007
 
4.3%
administrative 7731
 
0.7%
miscellaneous 7731
 
0.7%
non-operational 3409
 
0.3%
Other values (14) 10430
 
0.9%

Most occurring characters

ValueCountFrequency (%)
n 1093646
12.0%
i 972343
10.7%
903820
9.9%
e 842649
9.2%
t 717695
 
7.9%
c 607431
 
6.7%
I 539795
 
5.9%
p 527473
 
5.8%
o 491710
 
5.4%
s 441822
 
4.8%
Other values (25) 1990449
21.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9128833
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1093646
12.0%
i 972343
10.7%
903820
9.9%
e 842649
9.2%
t 717695
 
7.9%
c 607431
 
6.7%
I 539795
 
5.9%
p 527473
 
5.8%
o 491710
 
5.4%
s 441822
 
4.8%
Other values (25) 1990449
21.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9128833
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1093646
12.0%
i 972343
10.7%
903820
9.9%
e 842649
9.2%
t 717695
 
7.9%
c 607431
 
6.7%
I 539795
 
5.9%
p 527473
 
5.8%
o 491710
 
5.4%
s 441822
 
4.8%
Other values (25) 1990449
21.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9128833
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1093646
12.0%
i 972343
10.7%
903820
9.9%
e 842649
9.2%
t 717695
 
7.9%
c 607431
 
6.7%
I 539795
 
5.9%
p 527473
 
5.8%
o 491710
 
5.4%
s 441822
 
4.8%
Other values (25) 1990449
21.8%

latitude
Real number (ℝ)

High correlation  Zeros 

Distinct22108
Distinct (%)9.2%
Missing253
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean40.315141
Minimum0
Maximum40.912822
Zeros2416
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-11-19T02:55:19.230853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.60014
Q140.682931
median40.729807
Q340.760697
95-th percentile40.850955
Maximum40.912822
Range40.912822
Interquartile range (IQR)0.077766154

Descriptive statistics

Standard deviation4.0710132
Coefficient of variation (CV)0.10097976
Kurtosis94.054545
Mean40.315141
Median Absolute Deviation (MAD)0.035899198
Skewness-9.7992694
Sum9652170.3
Variance16.573148
MonotonicityNot monotonic
2024-11-19T02:55:19.404389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2416
 
1.0%
40.64831283 348
 
0.1%
40.58229742 332
 
0.1%
40.75977791 327
 
0.1%
40.73384018 297
 
0.1%
40.69082623 244
 
0.1%
40.758502 224
 
0.1%
40.74186904 224
 
0.1%
40.86590541 178
 
0.1%
40.60992885 175
 
0.1%
Other values (22098) 234653
97.9%
(Missing) 253
 
0.1%
ValueCountFrequency (%)
0 2416
1.0%
40.49956271 5
 
< 0.1%
40.50806852 4
 
< 0.1%
40.50911465 11
 
< 0.1%
40.50917535 8
 
< 0.1%
40.5099219 5
 
< 0.1%
40.50993835 8
 
< 0.1%
40.50999021 1
 
< 0.1%
40.51062322 8
 
< 0.1%
40.51075743 17
 
< 0.1%
ValueCountFrequency (%)
40.91282233 14
< 0.1%
40.91047336 2
 
< 0.1%
40.91001188 14
< 0.1%
40.90982301 15
< 0.1%
40.90732865 9
< 0.1%
40.90722436 7
< 0.1%
40.90688982 7
< 0.1%
40.90671944 5
 
< 0.1%
40.90664757 10
< 0.1%
40.9065722 13
< 0.1%

longitude
Real number (ℝ)

High correlation  Zeros 

Distinct22108
Distinct (%)9.2%
Missing253
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean-73.195859
Minimum-74.248708
Maximum0
Zeros2416
Zeros (%)1.0%
Negative237002
Negative (%)98.9%
Memory size1.8 MiB
2024-11-19T02:55:19.679880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-74.248708
5-th percentile-74.016176
Q1-73.989255
median-73.957175
Q3-73.897027
95-th percentile-73.79157
Maximum0
Range74.248708
Interquartile range (IQR)0.092228584

Descriptive statistics

Standard deviation7.3906524
Coefficient of variation (CV)-0.10097091
Kurtosis94.088567
Mean-73.195859
Median Absolute Deviation (MAD)0.038398436
Skewness9.8018994
Sum-17524406
Variance54.621743
MonotonicityNot monotonic
2024-11-19T02:55:19.852364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2416
 
1.0%
-73.7882815 348
 
0.1%
-74.16905259 332
 
0.1%
-73.82923543 327
 
0.1%
-73.87157703 297
 
0.1%
-73.98345225 244
 
0.1%
-73.83324181 224
 
0.1%
-74.00471301 224
 
0.1%
-73.83042975 178
 
0.1%
-73.92228162 175
 
0.1%
Other values (22098) 234653
97.9%
(Missing) 253
 
0.1%
ValueCountFrequency (%)
-74.24870792 6
 
< 0.1%
-74.24850215 17
< 0.1%
-74.24843447 2
 
< 0.1%
-74.24837218 8
< 0.1%
-74.24801199 10
< 0.1%
-74.24661164 11
< 0.1%
-74.24646442 8
< 0.1%
-74.24392834 8
< 0.1%
-74.24392109 5
 
< 0.1%
-74.24266267 3
 
< 0.1%
ValueCountFrequency (%)
0 2416
1.0%
-73.70092806 11
 
< 0.1%
-73.70171187 9
 
< 0.1%
-73.70268132 13
 
< 0.1%
-73.70269217 1
 
< 0.1%
-73.70272112 13
 
< 0.1%
-73.70272827 7
 
< 0.1%
-73.70274635 11
 
< 0.1%
-73.70276092 16
 
< 0.1%
-73.70278986 21
 
< 0.1%

community_board
Real number (ℝ)

High correlation  Missing 

Distinct69
Distinct (%)< 0.1%
Missing2875
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean255.23934
Minimum101
Maximum595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-11-19T02:55:20.027894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile102
Q1106
median302
Q3401
95-th percentile413
Maximum595
Range494
Interquartile range (IQR)295

Descriptive statistics

Standard deviation129.79592
Coefficient of variation (CV)0.50852632
Kurtosis-1.4134476
Mean255.23934
Median Absolute Deviation (MAD)105
Skewness0.076842019
Sum60439654
Variance16846.981
MonotonicityNot monotonic
2024-11-19T02:55:20.215117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 17739
 
7.4%
103 11799
 
4.9%
102 10846
 
4.5%
104 9273
 
3.9%
407 8976
 
3.7%
301 8066
 
3.4%
101 7136
 
3.0%
106 7108
 
3.0%
401 6800
 
2.8%
108 6640
 
2.8%
Other values (59) 142413
59.4%
ValueCountFrequency (%)
101 7136
3.0%
102 10846
4.5%
103 11799
4.9%
104 9273
3.9%
105 17739
7.4%
106 7108
3.0%
107 5273
 
2.2%
108 6640
 
2.8%
109 2094
 
0.9%
110 1718
 
0.7%
ValueCountFrequency (%)
595 5
 
< 0.1%
503 2079
0.9%
502 2954
1.2%
501 3624
1.5%
483 256
 
0.1%
482 4
 
< 0.1%
481 47
 
< 0.1%
480 51
 
< 0.1%
414 992
 
0.4%
413 2865
1.2%

council_district
Real number (ℝ)

High correlation  Missing 

Distinct51
Distinct (%)< 0.1%
Missing2865
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean20.987893
Minimum1
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-11-19T02:55:20.378970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median20
Q335
95-th percentile48
Maximum51
Range50
Interquartile range (IQR)31

Descriptive statistics

Standard deviation15.877508
Coefficient of variation (CV)0.75650795
Kurtosis-1.3187543
Mean20.987893
Median Absolute Deviation (MAD)16
Skewness0.22742685
Sum4970059
Variance252.09526
MonotonicityNot monotonic
2024-11-19T02:55:20.547472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 19147
 
8.0%
1 17632
 
7.4%
4 16096
 
6.7%
2 12410
 
5.2%
33 7795
 
3.3%
20 7206
 
3.0%
34 6985
 
2.9%
26 6708
 
2.8%
39 6385
 
2.7%
43 5579
 
2.3%
Other values (41) 130863
54.6%
ValueCountFrequency (%)
1 17632
7.4%
2 12410
5.2%
3 19147
8.0%
4 16096
6.7%
5 4596
 
1.9%
6 4968
 
2.1%
7 3538
 
1.5%
8 3560
 
1.5%
9 2245
 
0.9%
10 3647
 
1.5%
ValueCountFrequency (%)
51 2585
1.1%
50 3129
1.3%
49 3079
1.3%
48 3176
1.3%
47 3319
1.4%
46 2656
1.1%
45 2654
1.1%
44 2010
 
0.8%
43 5579
2.3%
42 1576
 
0.7%

census_tract
Real number (ℝ)

High correlation  Missing 

Distinct1174
Distinct (%)0.5%
Missing2865
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean29849.808
Minimum100
Maximum162100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-11-19T02:55:20.716066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile1800
Q18000
median17300
Q342300
95-th percentile94600
Maximum162100
Range162000
Interquartile range (IQR)34300

Descriptive statistics

Standard deviation31273.797
Coefficient of variation (CV)1.0477051
Kurtosis2.6022362
Mean29849.808
Median Absolute Deviation (MAD)11600
Skewness1.6826656
Sum7.0686137 × 109
Variance9.7805035 × 108
MonotonicityNot monotonic
2024-11-19T02:55:20.895541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87100 2185
 
0.9%
6500 1952
 
0.8%
2100 1826
 
0.8%
2900 1810
 
0.8%
4100 1768
 
0.7%
700 1687
 
0.7%
3800 1644
 
0.7%
3300 1530
 
0.6%
7600 1487
 
0.6%
900 1472
 
0.6%
Other values (1164) 219445
91.6%
(Missing) 2865
 
1.2%
ValueCountFrequency (%)
100 435
0.2%
200 265
0.1%
201 34
 
< 0.1%
202 67
 
< 0.1%
300 376
0.2%
301 38
 
< 0.1%
400 89
 
< 0.1%
500 3
 
< 0.1%
501 107
 
< 0.1%
502 160
 
0.1%
ValueCountFrequency (%)
162100 43
 
< 0.1%
161700 197
0.1%
157903 22
 
< 0.1%
157902 134
0.1%
157901 93
< 0.1%
157101 100
< 0.1%
155102 180
0.1%
155101 34
 
< 0.1%
152902 49
 
< 0.1%
152901 48
 
< 0.1%

bin
Real number (ℝ)

High correlation  Missing 

Distinct19235
Distinct (%)8.2%
Missing4099
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean2584827.6
Minimum1000000
Maximum5799501
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-11-19T02:55:21.077837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1000000
5-th percentile1005764
Q11051842
median3022822
Q34007881
95-th percentile4532160
Maximum5799501
Range4799501
Interquartile range (IQR)2956039

Descriptive statistics

Standard deviation1345464.7
Coefficient of variation (CV)0.52052397
Kurtosis-1.4336572
Mean2584827.6
Median Absolute Deviation (MAD)1130022
Skewness0.087172864
Sum6.08913 × 1011
Variance1.8102752 × 1012
MonotonicityNot monotonic
2024-11-19T02:55:21.260390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4000000 899
 
0.4%
1000000 427
 
0.2%
3000000 359
 
0.1%
5039658 332
 
0.1%
4113546 327
 
0.1%
4045999 313
 
0.1%
1012541 247
 
0.1%
3397861 244
 
0.1%
4112276 236
 
0.1%
5000000 215
 
0.1%
Other values (19225) 231973
96.8%
(Missing) 4099
 
1.7%
ValueCountFrequency (%)
1000000 427
0.2%
1000003 16
 
< 0.1%
1000005 62
 
< 0.1%
1000006 14
 
< 0.1%
1000008 14
 
< 0.1%
1000009 10
 
< 0.1%
1000012 13
 
< 0.1%
1000014 27
 
< 0.1%
1000018 3
 
< 0.1%
1000021 13
 
< 0.1%
ValueCountFrequency (%)
5799501 16
 
< 0.1%
5174856 4
 
< 0.1%
5174558 2
 
< 0.1%
5171931 21
 
< 0.1%
5171653 58
< 0.1%
5170926 3
 
< 0.1%
5170602 6
 
< 0.1%
5170408 2
 
< 0.1%
5170220 5
 
< 0.1%
5170018 2
 
< 0.1%

bbl
Real number (ℝ)

High correlation 

Distinct18950
Distinct (%)7.9%
Missing459
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean2.478926 × 109
Minimum1
Maximum5.2700005 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-11-19T02:55:21.436877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.00201 × 109
Q11.0112501 × 109
median3.00825 × 109
Q34.00515 × 109
95-th percentile4.1229201 × 109
Maximum5.2700005 × 109
Range5.2700005 × 109
Interquartile range (IQR)2.9939 × 109

Descriptive statistics

Standard deviation1.332146 × 109
Coefficient of variation (CV)0.53738838
Kurtosis-1.344881
Mean2.478926 × 109
Median Absolute Deviation (MAD)1.04236 × 109
Skewness0.067925953
Sum5.9298884 × 1014
Variance1.7746131 × 1018
MonotonicityNot monotonic
2024-11-19T02:55:21.621954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1772
 
0.7%
4 855
 
0.4%
3 513
 
0.2%
2 372
 
0.2%
5024000180 332
 
0.1%
4050190005 327
 
0.1%
4018600100 313
 
0.1%
4142600001 256
 
0.1%
1007130001 247
 
0.1%
3001497501 244
 
0.1%
Other values (18940) 233981
97.6%
(Missing) 459
 
0.2%
ValueCountFrequency (%)
1 1772
0.7%
2 372
 
0.2%
3 513
 
0.2%
4 855
0.4%
5 128
 
0.1%
1000000000 2
 
< 0.1%
1000010010 3
 
< 0.1%
1000020001 86
 
< 0.1%
1000020002 16
 
< 0.1%
1000030001 4
 
< 0.1%
ValueCountFrequency (%)
5270000501 16
< 0.1%
5080470016 8
< 0.1%
5080460001 8
< 0.1%
5080430015 1
 
< 0.1%
5080340020 5
 
< 0.1%
5080260008 6
 
< 0.1%
5080260005 17
< 0.1%
5080260003 8
< 0.1%
5080200116 13
< 0.1%
5080200059 3
 
< 0.1%

nta
Text

Missing 

Distinct193
Distinct (%)0.1%
Missing2875
Missing (%)1.2%
Memory size1.8 MiB
2024-11-19T02:55:22.123613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters947184
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQN43
2nd rowQN28
3rd rowMN23
4th rowBK79
5th rowQN17
ValueCountFrequency (%)
mn17 13285
 
5.6%
mn13 6934
 
2.9%
mn24 6770
 
2.9%
mn23 6646
 
2.8%
mn27 5402
 
2.3%
mn22 5203
 
2.2%
qn22 5096
 
2.2%
mn15 4489
 
1.9%
mn25 4436
 
1.9%
mn19 4139
 
1.7%
Other values (183) 174396
73.6%
2024-11-19T02:55:22.771255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 142110
15.0%
2 89485
9.4%
M 86454
9.1%
B 86024
9.1%
3 73119
 
7.7%
1 70450
 
7.4%
K 64848
 
6.8%
Q 55656
 
5.9%
7 52790
 
5.6%
4 39720
 
4.2%
Other values (8) 186528
19.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 947184
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 142110
15.0%
2 89485
9.4%
M 86454
9.1%
B 86024
9.1%
3 73119
 
7.7%
1 70450
 
7.4%
K 64848
 
6.8%
Q 55656
 
5.9%
7 52790
 
5.6%
4 39720
 
4.2%
Other values (8) 186528
19.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 947184
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 142110
15.0%
2 89485
9.4%
M 86454
9.1%
B 86024
9.1%
3 73119
 
7.7%
1 70450
 
7.4%
K 64848
 
6.8%
Q 55656
 
5.9%
7 52790
 
5.6%
4 39720
 
4.2%
Other values (8) 186528
19.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 947184
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 142110
15.0%
2 89485
9.4%
M 86454
9.1%
B 86024
9.1%
3 73119
 
7.7%
1 70450
 
7.4%
K 64848
 
6.8%
Q 55656
 
5.9%
7 52790
 
5.6%
4 39720
 
4.2%
Other values (8) 186528
19.7%

grade
Categorical

Missing 

Distinct6
Distinct (%)< 0.1%
Missing123298
Missing (%)51.4%
Memory size1.8 MiB
A
80907 
B
14370 
C
9069 
N
 
7866
Z
 
3458

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters116373
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowA
3rd rowA
4th rowB
5th rowA

Common Values

ValueCountFrequency (%)
A 80907
33.8%
B 14370
 
6.0%
C 9069
 
3.8%
N 7866
 
3.3%
Z 3458
 
1.4%
P 703
 
0.3%
(Missing) 123298
51.4%

Length

2024-11-19T02:55:22.939804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T02:55:23.080428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
a 80907
69.5%
b 14370
 
12.3%
c 9069
 
7.8%
n 7866
 
6.8%
z 3458
 
3.0%
p 703
 
0.6%

Most occurring characters

ValueCountFrequency (%)
A 80907
69.5%
B 14370
 
12.3%
C 9069
 
7.8%
N 7866
 
6.8%
Z 3458
 
3.0%
P 703
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 116373
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 80907
69.5%
B 14370
 
12.3%
C 9069
 
7.8%
N 7866
 
6.8%
Z 3458
 
3.0%
P 703
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 116373
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 80907
69.5%
B 14370
 
12.3%
C 9069
 
7.8%
N 7866
 
6.8%
Z 3458
 
3.0%
P 703
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 116373
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 80907
69.5%
B 14370
 
12.3%
C 9069
 
7.8%
N 7866
 
6.8%
Z 3458
 
3.0%
P 703
 
0.6%

grade_date
Date

Missing 

Distinct1148
Distinct (%)1.1%
Missing131148
Missing (%)54.7%
Memory size1.8 MiB
Minimum2019-01-03 00:00:00
Maximum2024-09-30 00:00:00
2024-11-19T02:55:23.246982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T02:55:23.431979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-11-19T02:15:57.896916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:04:26.029896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:06:09.615214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:08:45.874144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:12:28.453933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:17:33.322514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:24:22.819640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:33:16.132524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:44:38.701067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:58:47.161381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T02:17:50.066929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:04:34.403590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:06:22.476337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:09:04.964018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:12:54.683916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:18:09.227197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:25:09.779549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:34:17.757571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:45:55.502194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T02:00:22.177438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T02:19:45.972661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:04:43.026329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:06:36.260253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:09:24.602172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:13:21.913652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:18:46.319589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:25:57.822301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:35:20.342618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:47:14.013729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T02:01:58.825122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T02:21:42.821917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:04:52.065029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:06:50.378195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:09:45.134254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:13:49.720008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:19:24.686219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:26:47.715873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:36:24.037218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:48:34.481869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T02:03:37.116026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T02:23:41.918533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:05:01.672310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:07:05.442215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:10:06.185289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:14:18.838880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:20:03.816929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:27:39.032893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:37:29.700742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:49:56.481644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T02:05:17.151197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T02:25:43.387845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:05:11.716930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:07:20.753110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:10:27.813175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:14:48.806449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:20:43.457485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:28:31.518535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:38:36.967803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:51:20.601496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T02:06:58.858694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T02:27:46.313722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:05:22.649768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:07:36.533188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:10:50.077431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:15:19.699450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:21:24.893524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:29:25.526547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:39:46.025551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:52:46.133770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T02:08:42.776850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T02:29:52.396176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:05:33.632079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:07:52.947628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:11:13.384237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:15:51.598329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:22:07.597321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:30:20.928360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:40:57.508000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:54:13.461204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T02:10:28.580653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T02:31:59.129679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:05:45.286244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:08:09.715515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:11:37.715961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:16:24.375897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:22:51.905412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:31:17.722818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:42:09.685795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:55:42.918123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T02:12:16.787597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T02:34:09.002457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:05:57.180938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:08:27.435193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:12:02.709593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:16:58.408746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:23:36.760246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:32:16.378176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:43:23.545078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T01:57:13.927860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T02:14:06.589886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-19T02:55:23.574135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
actionbblbinborocamiscensus_tractcommunity_boardcouncil_districtcritical_flaggradeinspection_typelatitudelongitudescorezipcode
action1.0000.0230.0250.0220.0250.0210.0240.0220.4490.4220.5480.0090.0090.2910.020
bbl0.0231.0000.9660.9930.0360.6270.9760.7740.0150.0300.023-0.3000.4880.0350.847
bin0.0250.9661.0001.0000.0410.5770.9570.7550.0140.0330.027-0.3600.4970.0310.835
boro0.0220.9931.0001.0000.0400.3661.0000.8610.0140.0330.0330.0470.0470.0410.762
camis0.0250.0360.0410.0401.0000.0210.0330.0310.0140.1230.232-0.0120.0090.1380.031
census_tract0.0210.6270.5770.3660.0211.0000.6050.5120.0100.0390.023-0.0630.6570.0360.680
community_board0.0240.9760.9571.0000.0330.6051.0000.7830.0160.0320.024-0.3560.5300.0370.852
council_district0.0220.7740.7550.8610.0310.5120.7831.0000.0150.0420.025-0.6330.1730.0210.718
critical_flag0.4490.0150.0140.0140.0140.0100.0160.0151.0000.1250.5780.0120.0120.1240.016
grade0.4220.0300.0330.0330.1230.0390.0320.0420.1251.0000.4770.0180.0180.5000.026
inspection_type0.5480.0230.0270.0330.2320.0230.0240.0250.5780.4771.0000.0240.0240.0660.026
latitude0.009-0.300-0.3600.047-0.012-0.063-0.356-0.6330.0120.0180.0241.0000.301-0.001-0.328
longitude0.0090.4880.4970.0470.0090.6570.5300.1730.0120.0180.0240.3011.0000.0330.637
score0.2910.0350.0310.0410.1380.0360.0370.0210.1240.5000.066-0.0010.0331.0000.045
zipcode0.0200.8470.8350.7620.0310.6800.8520.7180.0160.0260.026-0.3280.6370.0451.000

Missing values

2024-11-19T02:36:20.392173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-19T02:38:34.555344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-19T02:40:52.544097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

camisdbaborobuildingstreetzipcodephonecuisine_descriptioninspection_dateactionviolation_codeviolation_descriptioncritical_flagscorerecord_dateinspection_typelatitudelongitudecommunity_boardcouncil_districtcensus_tractbinbblntagradegrade_date
050099353LA AURORAQueens23917BRADDOCK AVE11426.07183474271Spanish2023-12-22T00:00:00.000Violations were cited in the following area(s).04LEvidence of mice or live mice in establishment's food or non-food areas.Critical44.02024-11-18T06:00:10.000Cycle Inspection / Initial Inspection40.726551-73.728606413.023.0162100.04167536.04.079870e+09QN43NaNNaN
150118899MAMA'S RESTAURANTQueens370873RD ST11372.03478917275Bangladeshi2023-05-10T00:00:00.000Violations were cited in the following area(s).04AFood Protection Certificate (FPC) not held by manager or supervisor of food operations.Critical53.02024-11-18T06:00:10.000Pre-permit (Operational) / Re-inspection40.748445-73.892648403.025.029100.04454471.04.012830e+09QN28C2023-05-10T00:00:00.000
250108824NY 99 CENTS FRESH PIZZAManhattan12PERRY STREET10014.09172922325Pizza2024-09-26T00:00:00.000Violations were cited in the following area(s).10GDishwashing and ware washing: Cleaning and sanitizing of tableware, including dishes, utensils, and equipment deficient.Not Critical26.02024-11-18T06:00:10.000Cycle Inspection / Initial Inspection40.735825-74.001259102.03.07100.01010884.01.006120e+09MN23NaNNaN
350132634AUTHENTIC FLAVAZBrooklyn1377EAST NEW YORK AVENUE11212.07189751121Caribbean2024-06-14T00:00:00.000Violations were cited in the following area(s).08AEstablishment is not free of harborage or conditions conducive to rodents, insects or other pests.Not Critical34.02024-11-18T06:00:10.000Cycle Inspection / Initial Inspection40.669334-73.917623316.041.036100.03039538.03.014740e+09BK79NaNNaN
450126098SUSHI TIMEQueens7242AUSTIN ST11375.03479560001Japanese2023-03-21T00:00:00.000Violations were cited in the following area(s).04LEvidence of mice or live mice in establishment's food or non-food areas.Critical9.02024-11-18T06:00:10.000Cycle Inspection / Initial Inspection40.718725-73.841296406.029.073700.04077933.04.032558e+09QN17NaNNaN
55010150665 KUHOBrooklyn170165 STREET11204.07182325688Japanese2023-06-14T00:00:00.000Violations were cited in the following area(s).04KEvidence of rats or live rats in establishment's food or non-food areas.Critical29.02024-11-18T06:00:10.000Cycle Inspection / Initial Inspection40.620303-73.992119311.043.025200.03133467.03.055460e+09BK28NaNNaN
650001769ZEN VEGETARIAN HOUSEBrooklyn773FLATBUSH AVENUE11226.07182822255Vegetarian2022-01-13T00:00:00.000Violations were cited in the following area(s).02BHot food item not held at or above 140º F.Critical28.02024-11-18T06:00:10.000Cycle Inspection / Initial Inspection40.653972-73.959520314.040.079602.03116183.03.050640e+09BK60NaNNaN
750000071GEORGIA DINERQueens80-26QUEENS BOULEVARD11373.07186519000American2024-04-18T00:00:00.000Violations were cited in the following area(s).02BHot TCS food item not held at or above 140 °F.Critical32.02024-11-18T06:00:10.000Cycle Inspection / Initial Inspection40.737681-73.882258404.030.047900.04057121.04.024720e+09QN50NaNNaN
841616179DARO'S PIZZAQueens44-25KISSENA BOULEVARD11355.07184455573Pizza2022-04-14T00:00:00.000Violations were cited in the following area(s).08AFacility not vermin proof. Harborage or conditions conducive to attracting vermin to the premises and/or allowing vermin to exist.Not Critical42.02024-11-18T06:00:10.000Cycle Inspection / Initial Inspection40.753377-73.822170407.020.085900.04117219.04.051920e+09QN22NaNNaN
941147318FEENEY'S PUBBrooklyn62015 AVENUE11220.09177547597American2022-11-01T00:00:00.000Violations were cited in the following area(s).04LEvidence of mice or live mice in establishment's food or non-food areas.Critical27.02024-11-18T06:00:10.000Cycle Inspection / Initial Inspection40.638247-74.017432307.038.012200.03144010.03.058010e+09BK32NaNNaN
camisdbaborobuildingstreetzipcodephonecuisine_descriptioninspection_dateactionviolation_codeviolation_descriptioncritical_flagscorerecord_dateinspection_typelatitudelongitudecommunity_boardcouncil_districtcensus_tractbinbblntagradegrade_date
23966150133971ALL INQueens4508PARSONS BLVD11355.07187220992Asian/Asian Fusion2023-04-10T00:00:00.000Violations were cited in the following area(s).04KEvidence of rats or live rats in establishment's food or non-food areas.Critical24.02024-11-18T06:00:10.000Pre-permit (Operational) / Initial Inspection40.755483-73.815440407.020.0120100.04117536.04.052050e+09QN52NaNNaN
23966250127731AVOCADO SUSHIStaten Island4906ARTHUR KILL ROAD10309.03474051970Japanese2024-03-04T00:00:00.000Violations were cited in the following area(s).02BHot TCS food item not held at or above 140 °F.Critical11.02024-11-18T06:00:10.000Cycle Inspection / Re-inspection40.522714-74.239249503.051.022600.05087205.05.075840e+09SI11A2024-03-04T00:00:00.000
23966350105066MAYAS SNACK BARBrooklyn310SAINT NICHOLAS AVENUE11237.03478895529Frozen Desserts2023-04-24T00:00:00.000Violations were cited in the following area(s).20-04“Choking first aid” poster not posted. “Alcohol and Pregnancy” warning sign not posted. Resuscitation equipment: exhaled air resuscitation masks (adult & pediatric), latex gloves, sign not posted.Not CriticalNaN2024-11-18T06:00:10.000Administrative Miscellaneous / Initial Inspection40.701099-73.910796304.037.043900.03076401.03.033380e+09BK77NaNNaN
23966450070527BLACK STONE COFFEE ROASTERSManhattan502HUDSON STREET10014.02129896131American2022-01-25T00:00:00.000Violations were cited in the following area(s).04JAppropriately scaled metal stem-type thermometer or thermocouple not provided or used to evaluate temperatures of potentially hazardous foods during cooking, cooling, reheating and holding.Critical0.02024-11-18T06:00:10.000Cycle Inspection / Re-inspection40.733149-74.006379102.03.07300.01011133.01.006190e+09MN23A2022-01-25T00:00:00.000
23966550131039WING LUCKBrooklyn252LIVONIA AVENUE11212.07183852100Chinese2024-07-25T00:00:00.000Violations were cited in the following area(s).02GCold TCS food item held above 41 °F; smoked or processed fish held above 38 °F; intact raw eggs held above 45 °F; or reduced oxygen packaged (ROP) TCS foods held above required temperatures except during active necessary preparation.Critical33.02024-11-18T06:00:10.000Cycle Inspection / Initial Inspection40.662594-73.908545316.041.091800.03082153.03.035900e+09BK81NaNNaN
23966650106128AFTERNOONManhattan33WEST 32 STREET10001.03475426323Korean2022-02-22T00:00:00.000Violations were cited in the following area(s).10BPlumbing not properly installed or maintained; anti-siphonage or backflow prevention device not provided where required; equipment or floor not properly drained; sewage disposal system in disrepair or not functioning properly.Not Critical0.02024-11-18T06:00:10.000Pre-permit (Operational) / Initial Inspection40.747594-73.986531105.04.07600.01015846.01.008340e+09MN17NaNNaN
23966750061229BEBE FRITAYBrooklyn1464ROCKAWAY PARKWAY11236.06462473242Caribbean2024-01-11T00:00:00.000Violations were cited in the following area(s).04LEvidence of mice or live mice in establishment's food or non-food areas.Critical28.02024-11-18T06:00:10.000Cycle Inspection / Initial Inspection40.644588-73.901727318.046.096800.03229685.03.081840e+09BK50NaNNaN
23966850102277KUNG FU TEAManhattan73CHRYSTIE STREET10002.09175828883Coffee/Tea2021-10-05T00:00:00.000Violations were cited in the following area(s).08CPesticide use not in accordance with label or applicable laws. Prohibited chemical used/stored. Open bait station used.Not Critical7.02024-11-18T06:00:10.000Pre-permit (Operational) / Initial Inspection40.717147-73.994372103.01.01600.01003945.01.003040e+09MN27A2021-10-05T00:00:00.000
23966950057589TOUS LES JOURSQueens3916PRINCE ST11354.07188881992Bakery Products/Desserts2022-01-27T00:00:00.000Violations were cited in the following area(s).08CPesticide use not in accordance with label or applicable laws. Prohibited chemical used/stored. Open bait station used.Not Critical24.02024-11-18T06:00:10.000Cycle Inspection / Re-inspection40.759478-73.832243407.020.087100.04539244.04.049738e+09QN22B2022-01-27T00:00:00.000
23967050105466LE PAIN QUOTIDIENManhattan41W 40TH ST10018.06462767589Other2024-06-25T00:00:00.000Violations were cited in the following area(s).08AEstablishment is not free of harborage or conditions conducive to rodents, insects or other pests.Not Critical31.02024-11-18T06:00:10.000Cycle Inspection / Initial Inspection40.753069-73.983881105.04.08400.0NaN1.000000e+00MN17NaNNaN

Duplicate rows

Most frequently occurring

camisdbaborobuildingstreetzipcodephonecuisine_descriptioninspection_dateactionviolation_codeviolation_descriptioncritical_flagscorerecord_dateinspection_typelatitudelongitudecommunity_boardcouncil_districtcensus_tractbinbblntagradegrade_date# duplicates
040365904MEE SUM CAFEManhattan26PELL STREET10013.02123495260Coffee/Tea2022-09-30T00:00:00.000Violations were cited in the following area(s).02ITCS food removed from cold holding or prepared from or combined with ingredients at room temperature not cooled by an approved method to 41 °F or below within 4 hours.Critical59.02024-11-18T06:00:10.000Cycle Inspection / Re-inspection40.714861-73.998200103.01.02900.01001782.01.001630e+09MN27C2022-09-30T00:00:00.0002
140369016VIAND COFFEE SHOPManhattan673MADISON AVENUE10065.02127516622Greek2022-09-19T00:00:00.000Violations were cited in the following area(s).06CFood, supplies, and equipment not protected from potential source of contamination during storage, preparation, transportation, display or service.Critical8.02024-11-18T06:00:10.000Cycle Inspection / Re-inspection40.764924-73.970416108.04.011401.01040850.01.013760e+09MN40A2022-09-19T00:00:00.0002
240369016VIAND COFFEE SHOPManhattan673MADISON AVENUE10065.02127516622Greek2023-04-18T00:00:00.000Violations were cited in the following area(s).04LEvidence of mice or live mice in establishment's food or non-food areas.Critical12.02024-11-18T06:00:10.000Cycle Inspection / Initial Inspection40.764924-73.970416108.04.011401.01040850.01.013760e+09MN40A2023-04-18T00:00:00.0002
340369521KNICKERBOCKER BAR & GRILLManhattan33UNIVERSITY PLACE10003.02122288490American2023-11-14T00:00:00.000Violations were cited in the following area(s).02BHot TCS food item not held at or above 140 °F.Critical11.02024-11-18T06:00:10.000Cycle Inspection / Initial Inspection40.731969-73.994451102.02.05900.01009090.01.005608e+09MN23A2023-11-14T00:00:00.0002
440372445OMONIA CAFEQueens32-20BROADWAY11106.07182746650Greek2024-08-26T00:00:00.000Violations were cited in the following area(s).04MLive roaches in facility's food or non-food area.Critical27.02024-11-18T06:00:10.000Cycle Inspection / Re-inspection40.761477-73.924360401.022.05900.04008332.04.006120e+09QN70Z2024-08-26T00:00:00.0002
540378035DUNKIN', BASKIN ROBBINSQueens15367HORACE HARDING EXPRESSWAY11367.07183584031Donuts2022-08-22T00:00:00.000Violations were cited in the following area(s).08AEstablishment is not free of harborage or conditions conducive to rodents, insects or other pests.Not Critical22.02024-11-18T06:00:10.000Cycle Inspection / Initial Inspection40.739396-73.815741407.020.083700.04141524.04.064410e+09QN62NaNNaN2
640378212CUCHIFRITOManhattan168EAST 116 STREET10029.02128764846Spanish2024-06-03T00:00:00.000Violations were cited in the following area(s).20-06Current letter grade or Grade Pending card not postedNot CriticalNaN2024-11-18T06:00:10.000Administrative Miscellaneous / Initial Inspection40.798286-73.940864111.08.018200.01052256.01.016430e+09MN34NaNNaN2
740379580COOPER TOWN DINERManhattan3391 AVENUE10003.02126779287American2022-08-09T00:00:00.000Violations were cited in the following area(s).10BAnti-siphonage or back-flow prevention device not provided where required; equipment or floor not properly drained; sewage disposal system in disrepair or not functioning properly. Condensation or liquid waste improperly disposed of.Not Critical12.02024-11-18T06:00:10.000Cycle Inspection / Initial Inspection40.734753-73.979948106.02.06400.01020526.01.009250e+09MN21A2022-08-09T00:00:00.0002
840380628PISA PIZZERIAQueens6568MYRTLE AVENUE11385.07183816368Pizza2023-03-22T00:00:00.000Violations were cited in the following area(s).02BHot TCS food item not held at or above 140 °F.Critical12.02024-11-18T06:00:10.000Cycle Inspection / Initial Inspection40.701313-73.888255405.030.062900.04090148.04.036980e+09QN19A2023-03-22T00:00:00.0002
940380826FIT CAFETERIA (BUILDING A )Manhattan227WEST 27 STREET10001.02122175770American2022-02-14T00:00:00.000Violations were cited in the following area(s).09CFood contact surface not properly maintained.Not Critical8.02024-11-18T06:00:10.000Cycle Inspection / Initial Inspection40.747120-73.994991105.03.09500.01014251.01.007770e+09MN17A2022-02-14T00:00:00.0002